Talk
What have Large Language Models actually learned about language
This talk will explore what exactly large language models (LLMs) have learned about languages, and why it matters. It will attempt an outline of an answer to how these models understand the structure of language, and how this compares to how humans think about language. To answer the question, it will look at how LLMs perform across a range of different languages, even those with smaller digital footprints, and what this implies about how they represent language.
It will contrast the results from this investigation with the latest findings from the field of "mechanistic interpretability." These findings offer insights into the inner workings of LLMs, offering clues about the fundamental differences between how we understand language and how language is represented inside the models.
Finally, it will suggest a need a new approach to LLM research that brings together a richer understanding of language and a systematic investigation into how LLMs perform across a variety of languages.